00:02 OK,
00:02 thanks everyone.
00:03 I guess we can get started and,
00:04 and,
00:04 and,
00:05 uh,
00:05 people will join as we,
00:07 as we
00:09 start the introductions here.
00:10 So hi,
00:11 hello everyone.
00:11 Uh my name is Dion Filmer,
00:12 I'm the director of the World Bank's Development Research Group.
00:15 Uh,
00:16 welcome to this uh policy research talk,
00:18 the last of 2022.
00:21 Um,
00:21 as many of you know,
00:22 these talks give us an opportunity to present the
00:24 work coming out of the World Bank's research group.
00:28 Uh,
00:28 with the goal of sharing the findings
00:30 with colleagues inside and outside the department,
00:32 uh,
00:33 along with others outside the World Bank.
00:34 And with that,
00:35 um,
00:36 let me welcome our audiences
00:38 on both Webex as well as YouTube.
00:41 Um,
00:41 today we have a slightly different format than our usual.
00:44 Uh,
00:44 we have 3 presenters who will be giving relatively brief presentations.
00:48 We have Erhan Artuk,
00:50 Klaus Deininger,
00:50 and Bob Rikers.
00:52 They'll each provide us an overview of their research,
00:55 uh,
00:55 into the costs of,
00:57 of the war in Ukraine on in terms of human well-being,
01:01 both within Ukraine itself,
01:03 as well as around the world.
01:05 Erhan is a senior economist in the trade and integration,
01:08 uh,
01:09 team,
01:09 uh,
01:10 and his research primarily focuses on international trade policy
01:13 and its effect on labor markets and jobs.
01:16 Uh,
01:16 Klaus,
01:17 uh,
01:17 is a lead economist in the sustainability infrastructure team.
01:21 Uh,
01:22 focuses,
01:23 uh,
01:23 on income and asset inequality and its
01:25 relationship to poverty reduction and growth,
01:28 land issues,
01:29 and capacity building,
01:30 uh,
01:30 for policy analysis and evaluation.
01:33 Finally,
01:33 Bob is a senior
01:34 senior economist in the trade and international integration team
01:39 whose research interests include state capture,
01:41 corruption,
01:42 and the distributional impacts of trade.
01:45 I'm really grateful and uh to welcome back
01:49 uh Carolina Sanchez as a discussant today.
01:52 Uh,
01:52 Carol is currently the director of strategy and operations
01:55 in Europe and Central Asia at the World Bank.
01:58 Prior to this assignment,
01:59 she was the global director of the poverty and Equity Global Practice,
02:03 in which she served as a discussant to one of our former previous uh
02:07 uh PRTs.
02:08 Uh,
02:08 before that assignment,
02:09 she was the practice manager for poverty and
02:11 equity in the Europe and Central Asia region.
02:14 Uh,
02:15 Carol has worked on operations,
02:17 policy advice,
02:17 and analytical activities in Eastern Europe,
02:19 Latin America,
02:20 and South Asia,
02:21 and she was a core team member,
02:24 uh,
02:24 of the team that worked on the 2012
02:27 World Development report Gender Equality and Development.
02:31 So I'll ask each speaker to speak for approximately 15 minutes,
02:34 after which we'll hear from Carol for 10 to 15 minutes.
02:38 Uh,
02:39 we'll conclude the session with Q&A from the audience.
02:42 Uh,
02:42 if you have a question,
02:43 please use the raised hand option in Webex or signal to me in the chat,
02:47 or raise your hand in the room,
02:48 go to the mic.
02:50 Um,
02:50 if you have a question,
02:51 and I will call on you.
02:52 Um,
02:53 if you follow me on YouTube,
02:55 uh,
02:55 please submit your question in the chat and that'll get,
02:58 uh,
02:58 relayed to me.
03:00 A reminder,
03:00 we are recording the session.
03:02 Um,
03:03 with that over to and I think we're gonna do,
03:06 uh,
03:07 Erhan,
03:08 Klaus,
03:08 and then Bob in that order and I will give you,
03:11 since the three of you and we need to keep on time,
03:13 I will give you uh some warning,
03:15 hope it's not too disruptive,
03:16 uh,
03:17 before 15 minutes,
03:18 OK?
03:18 OK.
03:19 Erhan,
03:19 over to you.
03:26 Uh,
03:27 thank you very much.
03:28 Uh,
03:29 I'm
03:31 Assuming that my slides are,
03:33 uh,
03:33 you can see online.
03:35 Um,
03:38 So this is a recent work,
03:40 uh,
03:40 with
03:41 I'm going to present our findings from a recent work with uh
03:45 Nicolas Gomez Par and Harun Unar.
03:48 Uh,
03:48 this study is about the impact of conflict in the eastern Ukraine,
03:52 uh,
03:53 between 2014 and 2019 before
03:56 the recent invasion.
03:58 So,
03:59 uh,
04:00 I just want to like
04:02 make sure that the,
04:03 the,
04:04 the,
04:04 the context is,
04:05 is,
04:05 is well understood because
04:07 the impact of the war will be much larger after the invasion.
04:10 That's what
04:11 you would expect.
04:13 Uh,
04:14 so what is the impact of conflict on welfare?
04:16 This is a very old question,
04:18 and we know that it has a very heavy toll on people,
04:21 and
04:22 we also know that it is difficult to account for non-monetary aspects
04:26 like death,
04:27 sexual violence,
04:28 erosion of social trust,
04:30 and
04:31 corruption in the institutions.
04:33 Those are all aspects which are difficult to keep track of.
04:37 And even if you want to look at the.
04:40 Uh,
04:40 regular or more conventional measures of impact,
04:44 GDP and other economic measures are often inaccurate because during
04:48 the time of war it's difficult to keep track of,
04:50 of these statistics.
04:52 So we will need a
04:54 more creative,
04:55 uh,
04:55 different approach to measure the impact rather than looking at the
04:59 conventional economic measures.
05:01 And the idea here is to use the
05:03 outflow migration data from the conflict areas.
05:06 So we are going to look at the,
05:08 uh.
05:10 Displaced people and try to figure out the welfare changes from that.
05:15 So this work is
05:17 Uh,
05:18 it's a follow up to our previous work with the with the same team.
05:22 Uh,
05:23 we started to work on this
05:25 type of topics with the refugees in Kenya and then
05:29 we
05:29 look at the impact of war in Syria
05:33 and
05:33 one thing that we learned from
05:35 that policy report is that.
05:38 It is very difficult
05:39 to explain
05:41 the outflow of migrants or refugees
05:45 from
05:46 by looking at just the economic
05:48 data.
05:48 So for example,
05:49 you are trying to look at the destruction of the
05:52 buildings,
05:52 and,
05:53 and if you just put them in a regular macro model,
05:56 that you will find that the impacts are not as large
05:59 as you would
06:00 predict
06:01 from what you observe from the refugee outflows.
06:05 So
06:06 that report.
06:08 We tried to explain the reasons and we
06:11 we concluded that it is because of the
06:14 institutional degradation and violence and other things,
06:17 so it was difficult to
06:19 assess the actual economic impact.
06:21 Then more recently we worked on
06:24 the recovery program in eastern Ukraine with the same team,
06:27 and this report gave us the context and
06:30 we
06:30 understood the environment better and we had access to data.
06:34 So let me just briefly explain
06:36 the context here.
06:39 Before the current invasion,
06:41 there was a conflict in the eastern part of Ukraine which is called Donbas.
06:46 Donbas means
06:47 Donetsk Basin.
06:48 It's a portmanteau of words,
06:50 and it is the center of Ukraine's declining mining industry,
06:54 and it was,
06:54 it used to be
06:56 one of the industrial centers in Ukraine,
06:58 and conflict started around 2014 after
07:02 then President.
07:04 Uh
07:05 was removed from the office by the Senate,
07:08 by the parliament,
07:09 and then separatists took control of the eastern parts of
07:13 Donbass,
07:14 and Donbass consists of two oblasts.
07:16 One of them is Donetsk and the other is Luhansk,
07:19 and separatists had the
07:22 control of the eastern part and the
07:24 western parts were controlled by the Ukrainian government.
07:28 And just to give you a context,
07:30 uh,
07:30 compared to the recent invasion,
07:32 this was a very low
07:34 intensity conflict,
07:36 or you cannot really compare it with the
07:39 other conflicts.
07:40 For example,
07:41 in Syria there were about
07:43 half a million people dead,
07:44 and
07:44 in this conflict
07:46 the numbers were much lower,
07:48 around
07:48 5000 people,
07:50 and then people could move easily during this time,
07:53 so it was relatively low intensity.
07:56 So,
07:56 but still we saw lots of people
08:00 moving away
08:01 from the conflict regions
08:03 and the idea is to use
08:05 these
08:06 outflows from these regions
08:08 to measure the economic impact of the conflict.
08:11 So how do we do it?
08:12 So we have a very simple intuition.
08:14 So
08:14 although this is.
08:17 This is very simple and illustrative exercise.
08:19 It really
08:21 shows the the the method,
08:22 how it works.
08:24 So
08:26 One of the,
08:27 sorry about that.
08:29 So one of the
08:31 main,
08:31 one of the
08:32 major methods that we use in migration literature
08:35 and also in the trade literature is to
08:38 regress outflows to income changes.
08:41 In this graph,
08:42 you see that on the x axis,
08:44 there are,
08:44 you see income changes.
08:46 So let's say
08:48 how much income changes from year to year in a particular region in Ukraine,
08:52 let's say that Donetsk.
08:54 And then on the y axis
08:55 you see the outflows from Donetsk.
08:58 So
08:59 this negative relationship shows that
09:01 as you increase the income,
09:03 the outflows from a particular region decreases.
09:07 So if there are more economic opportunities,
09:09 people are more likely to stay.
09:11 If the economic opportunities decline,
09:13 people leave.
09:14 So this is a very simple
09:16 idea from the migration literature.
09:19 And we actually estimate,
09:21 we could estimate this,
09:23 this.
09:25 This line
09:26 and we use it,
09:28 we use instrumental variables and we find that about 6% increase in income
09:32 in an oblast reduces outflows by 6%,
09:35 so it's about a
09:36 0.6 elasticity
09:39 and it's,
09:39 it is similar to other numbers that were found in
09:43 other research.
09:45 So,
09:45 so what is the idea?
09:46 The idea is really simple.
09:48 So you see this graph with outflows on the Y and income changes on the X axis,
09:53 just flip it.
09:54 Flip,
09:55 make the x axis outflows and Y axis income changes.
09:59 Then
10:00 you can map
10:01 outflows
10:03 to income changes.
10:06 So this
10:08 Reversion
10:11 Can tell us
10:12 the impact
10:14 of a conflict in a region and by looking at the outflows
10:17 we can predict
10:19 the
10:20 implied
10:21 welfare changes
10:22 from this graph.
10:23 So this is a very simple illustration.
10:25 I'll show you a more
10:27 accurate picture later on
10:30 and we can try to answer this question.
10:32 Assume that migration probability increased by 700%.
10:36 So this is how much the
10:37 outflows increase in during the during the conflict
10:40 then what does it say about welfare?
10:43 So this is how are we going,
10:45 how we are going to
10:46 analyze the question.
10:48 So this basic idea is a
10:50 is an old idea.
10:52 It's well established in the literature.
10:54 Started with Hosam Miller,
10:55 which is used for econometric,
10:57 as an econometric tool,
10:59 but we used it in a.
11:00 In a paper,
11:02 uh,
11:02 about 10 years ago,
11:04 uh,
11:04 to calculate
11:06 the.
11:08 The role of mobility
11:10 in workers' welfare
11:12 in a paper and also in trade it's also
11:15 well understood,
11:16 uh,
11:16 for example,
11:17 you can calculate the gains from trade by looking at the trade flows.
11:20 That's a paper by Arcolakis Kosino and Rodriguez Clare.
11:24 So this is well understood,
11:25 but it is
11:27 not,
11:28 uh,
11:28 it is
11:29 well established,
11:30 but it is not
11:32 really popular in the literature,
11:33 so we don't know that many papers actually trying to do.
11:37 Something like this,
11:38 so the.
11:40 The nice thing about this approach that
11:43 I'm going to present is that it is very general.
11:45 There is a model.
11:47 Uh,
11:48 behind it,
11:48 but it's not a black box.
11:49 It has almost no assumptions.
11:52 You can just
11:53 play with the parameters and
11:54 make it static or dynamic.
11:56 You can,
11:57 uh.
12:00 You can make it perfect foresight or agnostic
12:03 about expectation formation.
12:04 You can make it risk averse,
12:06 risk neutral at different time preferences.
12:08 So
12:09 the numbers that I'm going to present
12:11 will be
12:13 the welfare impact
12:15 as
12:16 predicted by the residents.
12:18 So we don't need to understand how they make their decisions.
12:22 So
12:23 we.
12:24 We just need to
12:25 assume that they are not making systematic errors,
12:28 that is rational,
12:30 and we don't know how they form their expectations.
12:33 OK,
12:34 so,
12:34 and it is so general,
12:36 it would give us the
12:38 setups that are popular in the literature,
12:40 so you can play with the parameters you can get,
12:42 for example,
12:42 Eaton Quorum model or
12:44 uh Stephen Redding's model,
12:45 so.
12:47 It,
12:48 it basically,
12:49 uh,
12:51 can be adjusted,
12:52 so our method can be adjusted to
12:54 almost any discrete choice model in the literature.
12:58 OK,
12:58 so
12:59 I'm just going to present the general idea now.
13:03 So we have a model
13:04 pretty much summarized by this
13:06 chart.
13:08 Uh,
13:09 we have a,
13:09 we have residents in Donetsk,
13:11 let's say,
13:12 and they have options to move,
13:13 so they can move to,
13:14 let's say Kiev City,
13:16 Chernihiv,
13:16 Lviv,
13:16 or Poland.
13:19 And
13:21 When they make their choice,
13:22 we don't assume if they make it based on a current value or future value or
13:28 how do they assess the impact.
13:29 Are they risk neutral or not,
13:31 so we don't assume anything about that.
13:33 So if you look at the
13:35 numbers before and after the conflict,
13:37 for example,
13:37 in the Kiev city,
13:38 you will see that
13:40 the migration probability to Kiev city per year
13:44 increased from 0.2%
13:46 to 2.2%. That's a 10 times increase.
13:50 So it is similar numbers for different
13:52 oblasts within the Ukraine,
13:54 and
13:54 unfortunately we don't have data,
13:57 for example,
13:57 for those who are going to Poland.
14:00 And
14:00 this method does not require knowing all the flaws.
14:03 As long as we have some flaws,
14:06 we can calculate the impact,
14:08 OK?
14:09 So here is the.
14:12 More detail on the same idea.
14:14 So
14:15 we have 3,
14:16 let's say oblasts,
14:18 and we see that the migration probabilities increase from
14:21 10 times to 5 times.
14:24 The probability of going to Kiev city increased 10 times to Chernihiv 5 times.
14:29 And what does it mean?
14:30 That means that utility
14:32 of staying in Donetsk is decreasing.
14:36 So you could say,
14:37 OK,
14:38 how do you know that utility
14:40 to going to Lviv is not increasing,
14:42 so they might be from Lviv they might be going to Poland.
14:46 Right,
14:46 so,
14:47 uh,
14:48 we have,
14:49 uh,
14:49 robustness tests and we calculate it using different,
14:53 uh,
14:53 corridors and
14:54 our results are very robust,
14:56 so we can
14:57 take care of these type of concerns
14:59 and we can just ignore,
15:00 for example,
15:01 if you think that there's a problem with Tel Liv,
15:03 you can just ignore that corridor and calculate the numbers using other,
15:06 other corridors.
15:07 So 7 times outflows means
15:10 a huge decline
15:12 in
15:12 the utility in Donetsk.
15:14 So.
15:15 The next question is what would be
15:18 the decline in income
15:21 to make the residents of Donbas as worse off as the conflict,
15:25 so we are trying to find this number.
15:29 Because since our calculations are based on utility,
15:32 it's not really,
15:33 we cannot really understand what does it mean for
15:36 the workers' uh residents' income without putting it into
15:40 a utility function.
15:42 So.
15:44 Uh
15:45 We invert the utility function more or less,
15:48 and we find that numbers for Donetsk
15:50 and Luhansk are similar and precisely estimated.
15:53 The standards errors are very small,
15:55 and
15:56 we just want to emphasize that
15:58 we don't take the impact.
16:01 Ukraine-wide impact into account.
16:04 So it could be the case that because of conflict,
16:06 maybe Ukraine is spending so much money
16:09 and so much effort
16:11 on the conflict.
16:11 The general GDP might be declining.
16:13 So we don't account for that.
16:15 So that's very important
16:16 and the exact impact depends on the structure of the utility function because
16:21 we are mapping
16:22 the income to utility.
16:23 If it's a risk averse individual,
16:25 it will be a utility function concave.
16:28 It will mean a different
16:30 income impact compared to a risk neutral
16:32 agent,
16:33 which would have a linear utility function.
16:35 And also it depends on
16:37 the
16:38 time discount.
16:40 So if you think that
16:41 people are deciding based on instantaneous shocks like current changes or
16:46 whether they are taking the future into account,
16:48 you'll get different results.
16:49 So we have
16:50 all these parameters covered,
16:52 we take them from the literature and
16:54 try different
16:55 numbers,
16:55 and we have a general picture.
16:58 So
16:59 if the losses are amortized for 10 years,
17:03 we find that with the risk averse
17:05 agents,
17:06 don't loss is equal to
17:08 31% to 40% of income for 10 years.
17:13 And
17:14 I think it is for me it's easier to.
17:19 Understand the numbers if they are
17:21 calculated as lifetime loss
17:24 if the agents are risk averse.
17:26 The loss is equal to 9% to 8%.
17:30 Of lifetime income.
17:32 So
17:33 if
17:33 we assume that the agents are risk averse,
17:36 they are losing about 10% of their income lifetime.
17:39 And if the agents are
17:41 risk neutral,
17:42 the loss is between 7% to 25% of their lifetime income.
17:46 So the income
17:48 loss,
17:48 the equivalent income loss that would make the residents as worse as the conflict
17:53 are huge,
17:54 even before
17:55 the
17:56 recent invasions.
17:58 OK,
17:59 so,
18:00 uh,
18:01 I would like to conclude,
18:02 uh,
18:03 we just showed that welfare impacts of
18:05 conflict can be estimated from migration outflows.
18:08 We need to have
18:09 a migration elastic parameter properly estimated using
18:13 instrumental
18:14 uh variables,
18:15 and we need to see the migration outflows before and after the conflict,
18:20 but we don't need to see these outflows for all corridors.
18:23 And the conflict in eastern Ukraine before the recent invasion
18:27 significantly reduced the welfare.
18:29 It is
18:31 between 7% to 25% of their lifetime income.
18:35 OK,
18:35 thank you.
18:38 Thanks,
18:39 Johan.
18:39 Um,
18:40 Carol,
18:40 I hope it's OK if we go just to the next one and then we'll,
18:43 we'll bring you in after the three short presentations.
18:45 Thank you.
18:48 Uh,
18:48 over to you,
18:49 Klaus.
18:56 OK,
18:56 thank you,
18:57 thank you very much.
18:58 Uh,
18:58 I'll go to the actual conflict and uh
19:02 talk a little bit about the impact on the agricultural sector,
19:05 and this is a paper that draws on the ideas of many people,
19:08 as you have seen.
19:09 I think I have 3 objectives.
19:11 The first one,
19:12 I think,
19:12 as Erhan mentioned,
19:13 we need data,
19:14 so I will demonstrate the use of imagery
19:17 to assess the conflict and its impact on area
19:20 grown at village level in near real time.
19:23 Second,
19:23 I link that to survey data to assess the
19:26 welfare and distributional aspects and the scope for intervention,
19:30 and we then try to illustrate how a digital
19:33 farmer registry and linked to administrative data can complement that
19:37 to target,
19:38 deliver,
19:39 and also evaluate agricultural support quickly and transparently,
19:43 and I hope that I will be able to do that in 15 minutes.
19:47 So
19:48 that of course one issue is the data.
19:50 This is a high resolution image on the craters that are created by
19:57 ordnance.
19:58 If we look at that with freely available imagery,
20:01 we have three types of damages.
20:04 One is burns,
20:05 and so here you see the time
20:07 time series of 3 sets of imagery.
20:10 In the middle one you see where the fire is actually burning.
20:13 And then the burned area on the right hand side,
20:16 the same,
20:16 this is also burns.
20:18 The second type of issue is that whatever heavy
20:23 vehicles actually drive on your fields,
20:25 and the third type is that you have this ordnance and artillery fire.
20:30 This has all been done by a local university.
20:34 Classification in 2 means using Sentinel 2 freely available imagery
20:40 for the entire period.
20:42 And so the data that we got is here
20:45 we compared that with open source data that the ministry is making available on the
20:51 At village council levels,
20:53 I think all of this data is aggregated to 10,500 village councils.
20:58 Clearly what is evident here is that our data,
21:01 I mean,
21:02 so I think the first one is the ministry data.
21:04 It's much higher than what we have,
21:07 of course that is expected because you don't,
21:10 you may have conflicts in urban areas that don't filter
21:13 down to the rural areas or they don't cause field damage
21:16 and
21:17 it's also very weakly correlated.
21:19 We also compared that with the
21:23 ACL data,
21:23 which is the standard of conflict data globally.
21:26 On the picture here,
21:28 I think the Alet are the
21:30 green dots,
21:31 and clearly what is our data is both,
21:35 I think,
21:35 more granular.
21:37 It illustrates the
21:39 severity of the damage better and of course Alet is pushed,
21:43 pulled into the next village or the next settlement,
21:46 so that's why we have the settlement boundaries.
21:48 So I think the location is also less precise.
21:50 Than what we have,
21:52 then the second source of data is the national crop
21:54 classification map that we have been doing already before,
21:58 and that I think it links with what Iran,
22:00 I think that is for 4 years available nationally,
22:04 and the colors are different crops.
22:06 This has been done completely based on remote sensing,
22:09 no data from the government at all,
22:12 3.3 million fields based on sentinel imagery.
22:16 And of course the interesting part,
22:18 and I will come to that a little bit later,
22:20 is that we can link that to the cadaster to get
22:23 any farmers so we can identify from the cadaster the farms,
22:27 the parcels that any farmer cultivates
22:30 so we know their crop history for the last 4 years,
22:34 which is something that is quite interesting for the banks,
22:36 of course,
22:37 because if you have non-banked people who
22:40 At least you can see what they grew and also you can
22:43 get some estimate of the yields based on NDVI and others,
22:46 and I show that
22:47 you can also of course automate crop insurance
22:50 and that provides a basis for carbon credits.
22:53 So I think there are quite a lot of
22:56 applications and of course it's also
22:57 interesting for village councils to actually plan
23:00 in terms of reconstruction
23:03 and putting these data together,
23:04 I think this is just to frighten you.
23:08 What we get is that
23:10 essentially I think we have for the 40
23:16 that compared to other what they call consensus estimates
23:18 that are very weakly documented such as USDA.
23:21 Uh,
23:21 the area impact that we see is much less,
23:25 um,
23:26 but we see clear differences between,
23:28 so I think the panel A is the national million hectares,
23:31 so in 22
23:32 we have 8.3 and 17 million,
23:34 8.3 winter crops,
23:36 17 million summer crops.
23:38 Which is a little bit 11 or 5% less than the national average for the 4 years before,
23:44 for the three years before,
23:46 but at the village council level,
23:47 of course there is a clear difference,
23:50 and so we have both the crop damage,
23:52 the villages with crop damage,
23:54 and
23:55 the villages where there is any conflict reported,
23:57 and it shows that our data is actually
24:00 more precise.
24:01 Um,
24:02 but of course,
24:03 and I think then of course since we are talking agriculture,
24:06 we also need to take the climate into account.
24:08 This is only to show that 22 was a very dry year,
24:12 um,
24:13 and I don't want to bother you with GDDs
24:15 and all these things that the agronomists deal with
24:18 in terms of the methodology,
24:20 what we estimate is the area cultivated with winter or summer crop.
24:24 We also estimate yields for the winter crops,
24:27 but that I don't want to cover here.
24:29 We have a conflict indicator which is this one here,
24:33 so this is the
24:35 area damaged that will give us the direct conflict effect,
24:39 and we have a set of that X is a village that is
24:43 GDD rain plant in different seasons and higher growing higher order terms.
24:48 And then we have a time dummy and under the assumption that
24:52 with village fixed effects and our climatic variables we control for everything
24:56 that will give us the macro impact of the conflict.
24:59 And of course what we can then do is we can
25:03 simulate this with and without the macro effect or
25:06 with different putting different weather variables in there.
25:10 And if we do that,
25:11 and so I think that is the
25:14 and aggregate that in whatever ways we want to use this.
25:18 So just to show you the regressions,
25:19 I think there's nothing really extraordinary there,
25:22 but we see both for the winter crops,
25:24 both very significant impacts of the direct damage.
25:28 In terms of the conflict indicator,
25:31 open source data as well,
25:33 a very negative,
25:34 and we see a very negative year effect which all come together.
25:38 And of course for the summer crops we see the same thing.
25:41 I think for the summer crops we can distinguish,
25:43 of course the winter crops were planted before the conflict started,
25:47 so
25:48 the area affected,
25:49 the direct conflict effect on the area should be less.
25:53 Um,
25:53 and we also,
25:55 and I think an interesting part that is here is that the winter crop area,
25:59 there's actually some compensation
26:01 for
26:03 catching up in terms of where winter crop was either destroyed or failed.
26:09 People started growing summer crops and of course
26:10 that testifies to the resilience of the sector.
26:14 In terms of the predicted areas,
26:16 I think this is just then plugging in these estimates and
26:21 extrapolating.
26:22 So I think we find if there would have been no macro and no conflict,
26:26 we would have been 9 million
26:28 hectares of winter crop.
26:30 The conflict and macro effect is about slightly 9.5%,
26:34 and we can of course separate this out in terms
26:37 of both the net conflict and the macro effect separately.
26:41 I think we have similar figures here for the
26:44 or a slightly higher effect,
26:47 13% for the total
26:50 conflict effect
26:52 in
26:53 for the summer crops,
26:54 and of course we can distinguish that and I think of course one
26:57 interesting part is that we can actually
26:59 distinguish areas that are occupied by Russia
27:02 versus areas that are in the Ukraine proper.
27:07 There are a couple of extensions which I will not bother you.
27:09 Instead,
27:10 what I will do is go briefly into some
27:13 preliminary evidence from a survey that we have been doing
27:19 to actually get some of the welfare and distributional effects,
27:22 2500 farms,
27:23 the phone survey,
27:24 national coverage,
27:26 and different size strata so we can
27:30 look at differences across the farm size spectrum.
27:33 Um,
27:33 I'll show you three slides.
27:35 The first one is on the welfare.
27:37 What we see very clearly is a dramatic drop in terms of people's perspective from,
27:43 so I think we asked them a ladder of life between 1 to 10,
27:45 how do you judge your personal and the country's situation?
27:49 Uh,
27:49 it's particularly bad in the east and in the center,
27:53 and of the west,
27:55 surprisingly we went from the,
27:57 from the worst to actually being the best,
28:01 uh,
28:01 growth best perspectives.
28:03 Second,
28:03 but what is quite interesting is that the
28:06 country continues to function surprisingly well actually.
28:09 Social assistance increased or the share of people
28:13 getting,
28:14 and I think it was actually targeted quite well to the smaller farmers,
28:18 and also non-agricultural income also continues to be paid,
28:23 but of course the wars resulted in significant damage to
28:27 land and structures which is about equal to the.
28:31 What we get from the imagery in the east and the north,
28:33 but that also is geographically concentrated
28:36 in terms of the product.
28:38 But of course what we see,
28:39 so
28:40 if we look at the changes in area,
28:43 we get about 12% from the survey data,
28:45 which is quite close to what we get from our imagery analysis,
28:48 which is of course
28:50 gives us some comfort.
28:52 Interestingly,
28:53 that is all concentrated in the large farms,
28:55 all of the small guys.
28:57 are actually cultivating the same area,
29:00 but we see about 20% in terms of drop of physical yields,
29:05 and we see a very dramatic drop in terms of market integration.
29:10 I think we used wheat because that would have been marketed by now,
29:13 so clearly that is
29:15 all of the channels of exporting despite the grain deal and whatever.
29:19 It filters down significantly to the farm level,
29:23 which of course means that prices also have been dropped,
29:26 dropping significantly,
29:28 and that
29:29 of course that reinforces the pre-existing
29:32 differences across the farm size groups.
29:35 And of course I don't want to go into profits and production functions here,
29:39 but just
29:40 how to,
29:41 how does that actually link then to
29:43 to perspectives and the longer term outlook.
29:47 Interestingly enough,
29:48 we expected a lot of people actually willing to get out
29:52 like what Erhan said.
29:54 I think of the farm,
29:55 it seems that agricultural fundamentals are still very strong.
30:00 Only 4% are ready to sell the land and at a price well above what is the market price,
30:06 almost 80%.
30:08 And if you take out some of the small farms who are probably going out of farming,
30:12 in any case,
30:13 more than 80%
30:15 are willing to buy land and with a willingness to pay
30:18 that is in line with pre-war prices.
30:22 So that is quite interesting.
30:24 Also,
30:24 we see,
30:25 um,
30:26 and then of course I think some
30:28 in terms of background,
30:29 the World Bank has been pushing very hard for
30:32 opening of land sales markets in 2021.
30:35 That was done in July
30:37 2021,
30:38 just before,
30:39 so almost just before the war started.
30:41 What we see is that there is all the credit except for the smallest farm sized group,
30:47 which is probably going to consumption is going to working capital,
30:50 so there is no long term credit market at all.
30:53 Access to credit is extremely size biased.
30:56 It's the big guys
30:58 who are getting that,
30:59 and they're also paying much less interest
31:01 because the government is subsidizing this interest.
31:04 So of course that means that the mechanism in which the government
31:09 support is being distributed.
31:11 Invariably means,
31:13 I mean,
31:13 you need to get a loan and then the bank asks the government to reimburse
31:18 for the interest on that loan.
31:19 Of course that means none of the guys who are
31:21 not credit worthy will ever get any access to that,
31:23 and that is something that we are definitely we are discussing with the government
31:27 to actually change that with the land market and
31:30 with being able to use land as a collateral.
31:33 Actually that is,
31:34 and especially given that we see the high demand for borrowing.
31:38 It's definitely something that is important,
31:40 but of course what is interesting is that borrowing is not the only constraint.
31:45 I think we asked farmers what they would actually the government want to do.
31:50 And the most the top priority was to
31:52 regulate the input prices because there is very little
31:55 transparency and they're being ripped off.
31:59 So that brings me to the last point,
32:01 and I hope I still have 3 minutes left
32:04 to do that.
32:05 So the government response,
32:07 one of the immediate government responses with support from the EU
32:11 was to establish a 50 million cash grant scheme.
32:15 Establishing actually from scratch what they call a state agrarian registry,
32:19 which
32:20 was established in August,
32:22 which essentially links to all of the registries both to the national ID system
32:27 as well as to the
32:29 registry of rights and the cadaster
32:31 validates automatically if a farmer registers whether they
32:35 actually have the land registered in their name.
32:37 And then uses a cutoff point in terms of 120 hectares
32:42 to establish eligibility and of course the 120 hectares needs to be
32:46 in outside the conflict affected areas or outside the Russian territory
32:50 to check eligibility and using the crop map that I showed you earlier to
32:54 see whether that land was actually cultivated or not because they only want to give
32:59 the money to farmers that actually cultivated their land.
33:03 Surprisingly enough,
33:04 I think the ministry told us nobody will take
33:06 that thing and it will be a complete disaster.
33:10 There was that whole program was completely dispersed within 10 weeks
33:15 and by October,
33:16 people actually received their grant.
33:19 Interestingly enough,
33:19 we have about 55.
33:21 So of course that lends itself to doing some evaluation there
33:25 and it was completely transparent and I think we actually had a
33:30 Event yesterday with the minister where they was,
33:33 I think the bombs were flying over
33:35 them and they were in the bunker and I think they were quite happy about that.
33:40 Of course what and unfortunately due to the electricity shortages,
33:44 I cannot present you results already.
33:47 But we hope that in the next couple of weeks we will get them,
33:49 and of course we can then treat the parcels by treated and untreated farmers
33:54 to see whether they plant it or not.
33:56 We just have the first crop maps for the winter crop of the next 23 seasons,
34:02 and of course we can do panel estimation and compare to neighboring farms to see.
34:07 How to separate war from structural effects,
34:10 so I think that could be quite interesting and
34:12 of course that could also then help to inform
34:14 future policies in this area.
34:17 And of course,
34:18 given that we saw significant
34:21 imperfections in input markets as well and demand for technical assistance,
34:25 of course that means that definitely and the government also sees it
34:29 that way that this state registry could evolve into a central digital hub
34:34 for the reconstruction in agriculture that can.
34:36 And I think the banks are already asking us to pilot with that
34:41 access both state support and also
34:44 other
34:45 types of support or credit processing because of course for them
34:49 that provides a lot of
34:51 potential of checking their customers.
34:53 I think the one thing that is being discussed right now is access to the tax,
34:57 to the past tax
34:59 tax forms and statistical forms,
35:01 and if the banks have that,
35:02 I think that will be.
35:04 Very,
35:05 very positive.
35:06 So I think to conclude,
35:08 free satellite imagery provides an
35:12 important basis for policy decisions in conflict situations
35:16 because otherwise it's very difficult to go out into the field and
35:20 and collect data and it can be done very quickly.
35:23 But of course
35:25 getting distributional and welfare effects will still
35:28 require to get some additional information there.
35:31 And
35:32 I think what we see from the data that I've
35:34 shown you is that the war exacerbates the pre-existing inequalities
35:40 and that improving capital market and other
35:42 market functioning could actually provide an opportunity
35:46 to overcome this and of course that's where the link to digital registries and
35:52 both provides an opportunity for program design and implementation,
35:56 but also for targeting.
35:58 And evaluation and so I think for example,
36:01 one thing I mean
36:03 from the cases that are actually doubtful
36:06 in terms of cultivation,
36:07 of course we can use them for training data
36:09 to improve the predictions of the crop models.
36:12 So I think there's a lot of potential synergies
36:15 and obviously that could also provide opportunities
36:17 for future bank operations and analytical support,
36:20 which is something which we are discussing with our operational colleagues.
36:24 Thank you.
36:27 Thanks,
36:28 Kaus.
36:28 Obviously a very different uh
36:30 perspective and now we have uh even a third different perspective,
36:34 uh,
36:34 over to you,
36:35 Bob.
36:46 OK.
36:48 Thanks,
36:48 uh,
36:48 very much,
36:49 and,
36:49 uh,
36:50 thanks for having me.
36:51 So,
36:51 uh,
36:51 I think Claus's presentation leads naturally into my presentation which about is,
36:55 is about
36:56 the impact of,
36:58 uh,
36:58 the food price inflation that is induced by the war
37:01 on household welfare in developing countries.
37:03 So this is joint work with Erhan,
37:05 uh,
37:06 Guido,
37:07 and Guillermo Falcone and also Paula is here,
37:10 uh,
37:10 who's helped us,
37:11 uh,
37:11 a lot.
37:15 So,
37:16 Immediately after the onset of the of the war,
37:19 food prices spiked
37:21 quite dramatically.
37:22 So for instance,
37:23 the price of corn
37:24 in March
37:25 was 53% higher than it was in January,
37:29 and the price of,
37:29 uh,
37:31 sorry,
37:31 the price of wheat was 53% higher.
37:32 The price of corn was 23% higher.
37:35 And that's
37:36 because Ukraine and Russia are very important agricultural suppliers,
37:39 so they supply roughly 25%
37:41 of the world's wheat exports.
37:43 Um,
37:44 Russia is also very important,
37:46 in fact,
37:46 the most important exporter of fertilizer,
37:48 and Ukraine,
37:50 uh,
37:50 accounts for an important share of,
37:52 of oil seeds.
37:53 And so in this presentation I want to focus on
37:56 what the implications
37:57 of sort of this this big shock are
38:00 for
38:00 households in developing countries.
38:02 And in the first part I'm gonna take these price changes
38:05 as exogenous.
38:06 In the second part,
38:08 we will actually have a model
38:09 to simulate some of the impacts in which
38:11 we endogen endogenize some of these price changes.
38:16 So how do price changes impact households?
38:18 Well,
38:19 the impacts of course depend a lot
38:21 on
38:22 your consumption portfolios.
38:24 And you know how you earn your living
38:27 so as consumers
38:29 higher prices are bad news.
38:31 You have to pay more
38:32 um
38:33 for what you were consuming.
38:36 As
38:37 an income earner,
38:37 higher prices are good news.
38:39 So if you're a farmer,
38:40 uh,
38:41 or you're working in the agricultural sector,
38:43 these higher food prices could in fact
38:45 benefit you.
38:46 And so
38:47 for any given household,
38:48 sort of the net effect
38:49 of course depends on its
38:52 consumption and income portfolios,
38:53 at least in the short run
38:54 if we do not allow
38:56 adjustment
38:57 in the longer term,
38:58 households are gonna adjust their consumption
39:02 and production patterns
39:04 that's gonna impact trade.
39:05 And so
39:06 that will modulate the impacts that we're going to see.
39:10 So it's very important if you wanna analyze these price impacts
39:13 is knowing exactly
39:15 what households consume
39:16 and how they earn a living.
39:18 Unfortunately,
39:19 as a byproduct
39:20 of an
39:21 sort of earlier project that,
39:22 uh,
39:23 Aaron Guido and I have been working for
39:24 on for
39:26 now nearly a decade.
39:28 We've put together
39:30 Uh,
39:30 household survey data sets.
39:33 With extremely detailed price
39:36 information.
39:37 Uh,
39:39 for like a very sort of granular set of
39:42 products,
39:43 more than 53 products.
39:45 For
39:46 53 developing countries
39:48 and the Ukraine.
39:50 Uh,
39:51 basically for all low income countries for which we could get these data,
39:54 so the
39:55 requirement for inclusion in these data is that
39:59 the data have to be representative at the national level
40:01 and they have to cover
40:03 simultaneously both consumption decisions
40:05 and income decisions
40:07 because that allows us then to estimate,
40:09 you know,
40:10 the impact,
40:11 uh,
40:12 on any given households
40:13 and so these data are publicly available you can download them,
40:16 uh,
40:17 directly.
40:17 Here's the,
40:18 the link.
40:19 And so
40:20 what we learned from these data,
40:22 uh,
40:22 which is something you probably already know,
40:24 is that
40:25 poorer households tend to spend a greater share of their budget
40:28 on food items.
40:29 And that
40:30 means that they're more exposed
40:32 to food price inflation.
40:34 So just to give an example,
40:35 the plots here show you
40:38 how much they spend on wheat and corn respectively
40:41 with red sort of their expenditure shares,
40:44 and you can see that these are downward sloping
40:47 and then sort of like the,
40:48 the little green line at the bottom
40:50 is the income
40:52 share,
40:52 so how much income they earn
40:53 so poor households both spend more
40:56 on,
40:56 on,
40:56 on wheat.
40:57 And earn more from from wheat,
41:00 but
41:01 in aggregate,
41:01 uh.
41:03 The sort of their net budget share.
41:05 Decreases uh as a function of their income and that leaves them more exposed
41:09 similarly for for for corn.
41:11 And so if we simulate the impact
41:14 of
41:15 wheat and corn price
41:17 increases
41:18 on
41:19 real household incomes,
41:20 not allowing for any adjustments,
41:22 so
41:23 these are first order short term impacts.
41:26 Then what we see is that the average loss across these 53 developing countries
41:30 is roughly 2%,
41:32 but it's the poor that really suffered the brunt of this,
41:34 so they see their incomes decline much more on average
41:38 than than richer households.
41:40 That is not to say that no households gain.
41:42 So
41:42 in this bottom,
41:43 uh,
41:44 quantile
41:45 there are some households,
41:46 notably farmers,
41:48 people who produce these products,
41:50 that,
41:50 that will gain,
41:51 but on average
41:52 this impact is,
41:53 is very,
41:53 very negative.
41:54 And so on the whole,
41:56 food pliers inflation tends to erode real incomes and exacerbate
42:00 inequality.
42:02 Now of course you know these are results that are representative for
42:05 uh
42:06 basically.
42:07 Low income countries,
42:09 but you know as an
42:10 individual staff member you may be interested in your own country.
42:14 So
42:14 what we have done or really what Erhan has done
42:16 is
42:17 uh built
42:19 uh an online tool
42:20 that allows you
42:22 to do your own analysis to directly feed these price changes,
42:25 uh,
42:26 into,
42:27 uh,
42:28 this platform
42:30 and then
42:30 the website immediately gives you a sort of average welfare
42:34 effects.
42:34 So here's an example for Georgia.
42:36 Simulating sort of the very price sort of changes that
42:39 uh I just showed you
42:40 and and showing how they impact the distribution of of income.
42:44 So I,
42:44 I,
42:44 I hope that this is useful and I think what's neat about this is
42:47 that even though sort of we developed this sort of with the Ukraine war
42:50 in mind,
42:51 in principle
42:52 you can use this tool for any type of analysis that is going to impact prices.
42:56 So if you think about VAT reforms or other types of shocks
43:00 or subsidies,
43:01 uh,
43:01 I think this tool could be a very useful
43:03 starting point.
43:05 And of course the data are also publicly available.
43:07 So
43:07 if you don't like sort of what we've done or you want to make additional assumptions,
43:10 do more complex modeling,
43:12 it's all
43:13 possible.
43:14 So now I want to
43:15 talk a little bit about our own modeling
43:17 because one of the reasons why we see these price
43:19 spikes is precisely because of supply disruptions that sort of Klaus
43:23 uh talked about,
43:24 but also because many countries
43:26 responded to the war
43:28 by imposing bans.
43:29 So Russia
43:30 banned uh a lot of its exports of wheat,
43:33 corn,
43:34 fertilizers,
43:34 oil seeds,
43:35 among others,
43:36 and Ukraine
43:37 trade initially was sort of stifled completely,
43:39 but it also imposed bans.
43:41 Uh,
43:42 on its own exports
43:43 and then over time
43:44 we,
43:45 uh,
43:46 as sort of the conflict progressed,
43:47 other countries started to
43:49 respond.
43:50 So we also saw export bans,
43:51 for instance,
43:51 in Georgia,
43:52 Ghana,
43:53 India,
43:54 Moldova,
43:54 Kazakhstan,
43:55 and Kyrgyzstan.
43:56 Maybe not all of these are,
43:58 uh.
43:59 Exclusively motivated by the war,
44:02 uh,
44:03 because remember that the food prices are already
44:04 high because we're coming off of COVID,
44:07 um.
44:08 And perhaps because of climate change,
44:10 but they,
44:10 you know,
44:10 definitely sort of coincided sort of with the
44:13 evolution of this conflict.
44:15 So
44:16 To,
44:17 uh,
44:17 take these bans seriously and simulate their impact and also to simulate,
44:21 you know,
44:21 potential impacts of additional bans,
44:23 we developed,
44:24 uh,
44:25 a state of the art trade model
44:27 where the innovation is that we allow for heterogeneous
44:29 households and adjustments.
44:31 So,
44:31 so what's really new here relative sort of to what's out there in the literature
44:35 is that households are allowed to make supply and land allocation decisions.
44:39 And um
44:41 That's an innovation
44:42 and so
44:43 into this,
44:44 uh,
44:44 model we feed two types of data we use again,
44:47 uh,
44:48 our household impacts of tariff database to retrieve households income
44:53 and consumption shares for different products
44:55 and then we use trade data from the
44:56 international trade and production database for estimation.
44:59 And then we run two scenarios,
45:01 the baseline scenario.
45:03 It's simply that Russia bans export of a certain number of key commodities wheat,
45:08 corn,
45:09 fertilizer,
45:10 sugar,
45:10 and oil seeds.
45:12 And that Ukraine is
45:14 completely isolated in terms of its agricultural trade.
45:17 And then
45:18 in the second scenario we model the impact of retaliation.
45:22 So this is other countries.
45:24 That we know have imposed export restrictions,
45:27 uh,
45:27 uh,
45:28 imposing these bans.
45:30 So what are the results?
45:31 Well,
45:31 as you might expect,
45:33 the extent to which
45:35 like your food prices are gonna increase
45:38 is gonna be
45:39 very strongly correlated with how much you were trading
45:42 with the Ukraine,
45:44 uh,
45:44 and Russia before the war
45:45 and in particular the more you import for
45:48 from the Ukraine
45:49 and Russia,
45:49 the higher
45:51 your
45:52 your prices are gonna be.
45:53 So
45:53 in countries like Armenia and Georgia we see really dramatic price increases
45:58 and.
45:59 The this,
46:00 these price increases increases typically erode
46:03 welfare,
46:04 so,
46:04 so the bigger the price shock,
46:06 the more real income
46:07 you lose.
46:09 And
46:13 Those sort of
46:14 negative impacts are particularly pronounced for the poor.
46:17 So if we look at sort of the average welfare
46:20 of the top.
46:22 25% and compare that to the bottom
46:24 25%,
46:25 you can clearly see.
46:28 The poorer households
46:29 lose more,
46:30 which is
46:32 of course because
46:33 they spend a bigger share of their
46:35 budgets
46:36 on on food.
46:39 But I think what's needed is sort of like a standard trade model wouldn't give you.
46:43 These predictions
46:44 So
46:45 that is
46:46 new here.
46:48 So of course
46:49 this map shows you sort of the the average impacts
46:52 uh
46:53 by baseline scenario and
46:55 as you can see like the overwhelming majority of countries lose
46:59 and some countries like Mongolia and Armenia lose quite a lot.
47:02 On average they lose
47:03 2%.
47:05 There are also a few countries
47:06 that uh
47:07 gain a little bit.
47:09 So,
47:10 uh,
47:10 Pakistan and Iraq,
47:11 uh,
47:12 they gain.
47:13 Why?
47:13 Because they potentially can now ramp up their exports.
47:17 Then when we all
47:19 model the impact of sort of the additional
47:21 uh
47:22 export bans imposed by developing countries themselves,
47:24 you can see that these impacts,
47:26 these welfare losses.
47:28 Tend to get aggravated.
47:30 So in this scenario,
47:31 average welfare drops by 2.2% points.
47:37 And
47:37 that suggests or this that this retaliatory protectionism,
47:40 even if
47:41 it could be in your own interest.
47:44 Tends to amplify
47:45 The adverse effect of of this crisis and that's something that we believe should be
47:51 avoided.
47:52 So
47:52 to conclude,
47:53 uh,
47:54 this war induced food price inflation
47:56 hurts most developing countries.
47:58 I mean some gain,
47:59 but they only gain a little bit.
48:01 The impacts vary quite dramatically across
48:03 developing countries and and depend a lot
48:06 on your initial trade exposure.
48:10 Food price inflation is disequalizing
48:13 the poor suffer the most,
48:15 uh,
48:15 from this,
48:16 and retaliate protectionism
48:18 is making things worse
48:20 and therefore
48:21 should ideally be avoided.
48:23 And to end I also
48:25 really wanna highlight that sort of
48:27 this database that we've put together.
48:29 I really hope you will find it useful
48:31 and we also have tools that you can use to
48:33 to analyze the impacts
48:34 on the countries you're working on.
48:36 Thanks very much.
48:41 Thanks,
48:41 Bob.
48:42 Um,
48:42 clearly a running theme of exacerbation of inequality and,
48:46 and,
48:46 and hurting the poor.
48:48 Um,
48:48 with that,
48:49 let's turn over to,
48:50 to Carol,
48:51 um,
48:52 Uh,
48:52 for her reactions and comments and,
48:54 and,
48:55 you know,
48:55 both an apology and a thank you apology,
48:57 you know,
48:58 you got 33 very different
49:01 sets of analysis here to,
49:02 to,
49:03 uh,
49:03 to react to,
49:04 uh,
49:04 and thank you for taking that on.
49:06 Over to you,
49:06 Carol.
49:08 Well,
49:09 thank you Deonna,
49:10 thank you everybody for inviting me.
49:12 Uh,
49:12 it is truly fascinating work and I very much enjoyed,
49:15 you know,
49:15 looking through the materials and now listening to the presentations.
49:19 Um,
49:20 I thought that I would do the following and hopefully this is useful for everybody.
49:24 Um,
49:25 I wanna talk a little bit first about
49:28 what we are actually doing in Ukraine and,
49:30 and use that information as kind of the background or the context to talk,
49:35 you know,
49:35 to sort of reflect a little bit.
49:37 On the value added of of the kind of work that we're seeing here and how I see,
49:42 you know,
49:43 we are using it and we can use it further
49:45 to solve some of the challenges that we are
49:47 encountering in our engagement in Ukraine at the moment
49:51 and then I have some specific reflections and sort
49:53 of questions on each one of the presentations,
49:56 um,
49:57 so hopefully that hopefully that that works and,
49:59 and it helps bring everybody on the same page in terms
50:02 of where we are at the moment in in Ukraine.
50:05 Uh,
50:05 but I'm gonna be focusing,
50:06 you know,
50:06 with my new hat a little bit more on sort of the policy
50:09 and operational implications rather than some of
50:11 the technical aspects of the work,
50:12 although I do have,
50:13 uh,
50:14 questions on that as well,
50:15 and I'd love to follow up with the speakers.
50:18 So let me start with,
50:19 uh,
50:20 just a very quick overview of what we've been doing in
50:22 Ukraine over the last few months since the conflict started,
50:26 uh,
50:27 during the first few months of the conflict and up to basically about a month ago,
50:32 most of our engagement.
50:34 Has been in the form of fiscal support,
50:36 right?
50:37 Support to the budget
50:38 to help the government breach uh what's at this point a very large fiscal hole.
50:43 And a lot of that support has been focusing on pretty poor,
50:47 uh,
50:47 government services,
50:49 uh,
50:50 education,
50:50 health,
50:51 first respondents,
50:53 and also supporting some of the social transfers and the
50:55 pensions and Claus alluded a little bit to the,
50:58 to some of the government programs that are in place
51:00 to help,
51:01 uh,
51:01 farmers
51:02 and the way this is done is,
51:04 you know,
51:04 they spend the money we reimburse them for it after we verify it.
51:08 And we've actually managed to uh to sort of move uh quite
51:12 a significant amount of resources that way about 8 billion to date,
51:17 12 billion if you count,
51:18 um,
51:20 what was done sort of in the very early phases and
51:22 a lot of that is not necessarily World Bank resources,
51:24 it's resources from other donors,
51:26 primarily the US,
51:28 but they're being channeled through the bank
51:30 because we can provide that verification and we have this direct partnership
51:34 with the Ministry of Finance.
51:36 And I suspect that that will continue right this very direct
51:40 sort of line into the budget over the next few months,
51:43 but I think it's also become quite clear that um more
51:47 is needed and particularly that we need to start thinking about
51:51 supporting the recovery uh in in the parts of the
51:54 country where the conflict is not at least open conflict.
51:58 And really thinking about some priority areas for engagement where repairs
52:03 can happen and where we can resume or bring up activity
52:07 uh that has suffered from the conflict um so we've started to
52:10 think a little bit more about that and we're at the moment focusing
52:14 on health,
52:15 energy and transport and you'll see how actually some of that relates
52:19 quite directly to what we um
52:21 talked about today and I think as sort of the
52:24 next round of engagements we're probably looking into agriculture.
52:27 Uh,
52:28 possibly housing and education,
52:30 so the idea here is to provide,
52:32 you know,
52:33 support in some of these critical areas
52:35 to be able to disburse quickly,
52:37 but now we are not in the space of really supporting service provision,
52:40 really supporting sort of the purchases of
52:43 equipment that has got,
52:45 um,
52:45 that's been damaged or destroyed and,
52:47 and so on.
52:49 So,
52:50 that's what we are doing.
52:51 In doing that,
52:52 we've encountered
52:53 several challenges,
52:54 as you can imagine,
52:55 and this is where,
52:56 where the connection with some of the work starts to emerge.
53:00 The first challenge is that if you're gonna support,
53:04 you need to first have a sense for what are the impacts of the world,
53:07 right?
53:08 What sectors are affected,
53:09 what are the losses in terms of assets,
53:12 uh,
53:13 you know,
53:13 how are those geographically distributed,
53:16 etc.
53:16 right?
53:17 I mean you need to get a little bit of a photograph,
53:19 a picture of what's going on.
53:21 To do that,
53:22 um,
53:23 we conducted what we call a rapid damage and
53:25 needs assessment which tried to do exactly that,
53:28 assess damages and then think about
53:30 what's needed for recovery and reconstruction.
53:33 That was done initially with data up to June and now it's being updated.
53:38 With data up,
53:38 up to January,
53:39 but as you can imagine,
53:41 this is not your regular piece of,
53:43 you know,
53:43 analytical work because it was,
53:45 you know,
53:45 difficult for us to be there
53:47 because data is scarce,
53:49 uh,
53:50 because,
53:50 you know,
53:50 you can't quite conduct field work and,
53:53 and so on.
53:54 And at the same time,
53:55 the design of some of these projects that I was talking about,
53:58 it's obviously
54:00 very highly dependent on having a good understanding for,
54:03 you know,
54:03 what are some of the priority needs,
54:05 what are some of the locations
54:06 that have suffered these damages and that we can go to,
54:09 and also it's quite important for us moving forward to be able to monitor,
54:13 you know,
54:14 how the money that we are giving
54:16 is used,
54:16 whether the goods and services that are being purchased,
54:19 uh,
54:20 or financed,
54:20 you know,
54:21 are actually being delivered and so on.
54:24 So in that context as I look through these presentations um I think
54:28 a common theme that sort of runs through them from that perspective
54:32 is first
54:34 that in all cases the authors have been able to provide.
54:39 Up to date information
54:41 on various dimensions of the impacts of the conflict
54:44 on households or on a specific sectors
54:47 even in the absence of our ability to sort of be on the field so in that sense,
54:51 you know,
54:51 it's,
54:51 it's,
54:52 it really very nice illustrate very nicely illustrates
54:56 how you can
54:57 sort of,
54:57 you know,
54:58 triangulate and creatively use research and analysis
55:02 to answer some of these very operational questions
55:05 that we are dealing with at the moment.
55:08 Related to that,
55:09 I think it's also really nice to see how,
55:12 you know,
55:13 in,
55:13 in different ways the authors have actually used.
55:17 Data that is readily available,
55:18 but maybe not the kind of data that we normally tend to think about,
55:21 right?
55:21 Like surveys,
55:22 I'm thinking of clouds and,
55:24 and sort of the images that he's using
55:26 combining that with maybe data from the pre-conflict time
55:30 and again using that combination and some economic modeling and analysis
55:34 to provide um a very granular in some cases situ.
55:37 You know,
55:37 picture of what's happening on the ground.
55:40 So in that sense again
55:41 I think some of those commonalities were very interesting to me and I think,
55:45 you know,
55:45 I know in some cases I know Claus is very closely working with our education team,
55:49 but it would be very important for us to
55:51 ensure that that these connections are being made across,
55:54 you know,
55:54 all the different pieces with the teams that are working in the sectors.
55:58 So that's in terms of,
55:59 you know,
55:59 where we are,
56:00 uh,
56:01 what are the challenges that we're facing and how I see
56:03 the work presented today and similar work being extremely useful
56:06 for us as we move forward in the region.
56:09 Then,
56:10 talking a bit more specifically about each one of the three pieces and some
56:13 of the reflections that came to mind as I was reading through it.
56:16 So let me,
56:16 let me go in the order that they were presenting.
56:20 So the migration work,
56:22 um,
56:23 you know,
56:23 this is fascinating.
56:24 I,
56:24 I,
56:24 I think most of you know this,
56:26 but the war in Ukraine has really created
56:27 a massive amount of displacement within Europe,
56:30 uh,
56:31 both within Ukraine,
56:32 right?
56:32 People leaving their,
56:33 uh,
56:34 hometowns and moving to other areas of the country that are considered safer,
56:38 but also sort of leaving Ukraine
56:39 and particularly moving into some of the countries of the EU,
56:42 Poland,
56:43 but also others,
56:44 right?
56:46 So,
56:46 what are some of the,
56:47 the questions that,
56:48 you know,
56:49 looking at the migration analysis for me came to mind?
56:53 Basically reflecting on what's specific about this particular displacement,
56:57 displacement episode because I think it looks somewhat different from other
57:01 displacement episodes and migration episodes that we've seen in the past.
57:05 First,
57:06 when we look at who is moving,
57:08 it's mostly women and children.
57:10 So I wonder,
57:10 and this is a question for everyone.
57:13 What the role of demographics is in their analysis and,
57:17 and demographics in the sense that some of those people are income earners,
57:20 some of them are not,
57:21 does that have an impact in how you
57:22 think about those relationships that we described?
57:25 We also know that because this is happening
57:28 within a country that has a relatively sophisticated
57:31 financial system
57:32 and,
57:33 and where people are were able to save before the war
57:36 because you know income levels were such that uh that savings,
57:39 you know,
57:39 were possible for a large share of the population
57:42 when people leave the country they actually still
57:44 have assets have access to their financial assets,
57:47 right,
57:47 that is still withdrawing money from their bank accounts,
57:50 uh,
57:50 be it from other places in the country or actually from,
57:54 from abroad either.
57:56 Uh,
57:56 and in many cases they've been able,
57:58 or at least you know,
57:58 anecdotal evidence suggests that they've been able to continue their work,
58:02 uh,
58:03 for those of them that were able to telework,
58:05 uh,
58:05 and in the case of kids,
58:06 you know,
58:07 they've been they've been sort of continuing
58:08 their education because the Ministry of,
58:10 um,
58:11 Education in Ukraine very quickly put out,
58:13 put,
58:13 uh,
58:14 together a.
58:14 Sort of remote learning platform.
58:16 So again,
58:17 how do these considerations,
58:19 um,
58:19 how would that sort of help us understand the,
58:22 the analysis on this relationship between mobility and income shocks,
58:26 right?
58:26 These are some things that are very specific about what
58:28 what we're seeing there but uh that I think matter
58:31 in how we interpret the results
58:33 and then,
58:34 uh,
58:34 what are some of the emerging questions that we are facing as we think about how,
58:37 how to support,
58:38 uh,
58:38 these groups.
58:41 You know,
58:41 we know there's been income losses,
58:43 you talked about that as well,
58:44 but we also know that there are,
58:45 there's been loss of assets,
58:47 right?
58:47 Physical assets,
58:48 we don't know the magnitude of that,
58:49 but you know,
58:50 houses are being destroyed,
58:51 other things are being destroyed,
58:53 so this would indicate,
58:55 you know,
58:55 given your analysis that,
58:57 that we would
58:58 be looking at sort of maybe larger flows but also maybe more permanent flows.
59:03 I don't know it,
59:03 it's sort of a question,
59:04 how would you sort of think about that?
59:06 Also,
59:07 how do we think about sort of the short-term impacts,
59:09 mostly on income and the long-term impacts potentially on human capital,
59:13 right?
59:13 They've been able to sort of
59:14 supplement with this remote learning,
59:16 but as we know from COVID,
59:17 that's not a very satisfactory long-term solution.
59:20 And then I was wondering if you could reflect a little bit on
59:24 things that you mentioned in your introduction but maybe not talked about directly
59:27 in the context of the analysis that is other things that may actually affect
59:32 Both the decision to leave and the decision to return,
59:34 and those are sort of safety considerations,
59:37 uh,
59:37 but also what we hear,
59:38 for example,
59:38 from a lot of the migrants,
59:40 the people that have left is that they are paying attention to,
59:42 for example,
59:43 whether government services are resuming in certain areas,
59:46 are schools back up and running.
59:48 You know,
59:49 our clinics,
59:49 uh,
59:50 again working and that's influencing their decision,
59:52 not just whether they can go back to work
59:55 or not.
59:55 And of course there are family separation considerations and so on.
59:58 So again,
59:58 very interesting,
59:59 I'm just,
59:59 I'm just trying to map out,
1:00:01 you know,
1:00:01 what you're finding with some of the issues
1:00:03 that we are grappling with.
1:00:05 On the agriculture side,
1:00:07 um,
1:00:08 fascinating work again,
1:00:09 Claus,
1:00:10 and I know again you've,
1:00:11 you've been sort of instrumental in some of the work we've done on this,
1:00:14 on the rapid damage and needs assessment,
1:00:17 um,
1:00:17 and I completely agree with you that farmers
1:00:19 at the moment are facing multiple constraints,
1:00:21 right?
1:00:22 There are rising cost of inputs,
1:00:24 seed,
1:00:24 fertilizers,
1:00:25 there's lack of credit which you very directly talked about,
1:00:28 but there are a couple of things that you didn't mention that I think are important.
1:00:32 The first one is that.
1:00:35 Because
1:00:35 the war basically brought exports
1:00:38 to a,
1:00:39 to a halt,
1:00:40 and,
1:00:40 you know,
1:00:41 Bob in a way reflected on this a little bit in his presentation.
1:00:44 What happened is that the storage capacity
1:00:47 in country is basically mostly taken up by last year's crop.
1:00:52 So,
1:00:53 when farmers think about,
1:00:54 you know,
1:00:55 dynamically,
1:00:55 right?
1:00:56 Planting and then what's gonna happen once they have their crops,
1:01:00 they,
1:01:00 they do realize that there's a shortage of a storage capacity,
1:01:04 right?
1:01:05 And they also realize that exporting their products for
1:01:08 those of them who actually were market oriented,
1:01:10 it's become significantly harder because of the conflict.
1:01:14 So those are two factors that I think in addition
1:01:16 to the lack of credit that you talked about are
1:01:19 actually influencing or we expect will influence some of the
1:01:22 decisions that that farmers are making and I wondered if,
1:01:25 if there was a way to use
1:01:27 your analysis to,
1:01:28 to maybe tackle
1:01:29 some of those um so in a way I.
1:01:32 As we see the direct impact of the conflict which you talked about,
1:01:34 right,
1:01:34 in terms of destruction,
1:01:36 you know,
1:01:36 land being affected,
1:01:38 damaged,
1:01:38 des destroyed,
1:01:40 and then there are sort of the indirect impacts,
1:01:41 right,
1:01:42 through credit,
1:01:42 through storage,
1:01:43 through markets,
1:01:45 uh,
1:01:45 we see farm,
1:01:46 farm brigade prices having declined quite significantly.
1:01:50 Um,
1:01:51 so I was wondering whether,
1:01:52 for example,
1:01:52 I was thinking like how would you get to that,
1:01:54 right?
1:01:54 So I was thinking if for example,
1:01:57 Is it possible to gather information with the kind of
1:01:59 data you have about crops being left in the field,
1:02:02 right?
1:02:03 So maybe there are some farmers that on the basis of these restrictions are deci,
1:02:06 you know,
1:02:06 have decided that it's actually not worth to sort of,
1:02:09 you know,
1:02:09 harvest and,
1:02:10 and because of these additional sort of costs that are coming,
1:02:13 so it will be very interesting to hear about that.
1:02:15 And then finally,
1:02:16 uh,
1:02:17 on,
1:02:17 on the work on on inflation
1:02:20 uh
1:02:21 by Bob,
1:02:21 um,
1:02:22 you know,
1:02:22 again fascinating,
1:02:24 um,
1:02:25 and,
1:02:25 and you know with my previous sort of hat I've been reinforcing a lot of those
1:02:28 messages that we've tried to get across many
1:02:30 times that you know trade does have very important
1:02:33 distributional um impacts,
1:02:36 um,
1:02:36 here.
1:02:37 I think a couple of observations,
1:02:39 I mean you pointed out that Ukraine and Russia were incredibly important in terms of
1:02:43 global food markets and,
1:02:45 and it's obvious that when,
1:02:46 when they stopped supplying the graph that you showed,
1:02:49 you know,
1:02:49 we saw
1:02:50 prices rising but also I think,
1:02:52 and you didn't talk about that that much when,
1:02:54 when the Black Sea route sort of reopened.
1:02:58 Uh,
1:02:58 we also saw them coming down,
1:03:00 right,
1:03:01 even though the supply did not reach pre-war levels,
1:03:04 but obviously,
1:03:04 you know,
1:03:05 we had more stock
1:03:06 in,
1:03:07 in circulation,
1:03:08 uh,
1:03:08 and also there's been an effort to sort of channel some
1:03:10 of those exports via Europe instead of the Black Sea.
1:03:13 So hopefully all of that has eased
1:03:15 some of the restrictions,
1:03:17 and I was wondering if you've seen that in your
1:03:18 analysis to the extent that you can have more recent,
1:03:21 um,
1:03:23 analysis of the,
1:03:24 of the impacts on prices.
1:03:26 But I guess there are two questions that have come to mind
1:03:29 as,
1:03:29 as we've seen kind of
1:03:32 how,
1:03:32 how this sort of resuming of exports has evolved.
1:03:35 The first one is that I think we assume that as quantity varies,
1:03:39 it's reaching sort of the points.
1:03:42 that we wanted to reach,
1:03:43 or,
1:03:43 or I wonder if that's implicit in your analysis.
1:03:46 So for example,
1:03:46 there's been a lot of questions about whether
1:03:49 You know,
1:03:50 with exports resuming,
1:03:51 are they really sort of reaching the countries
1:03:53 that have suffered the most in your map,
1:03:55 you know,
1:03:55 Africa,
1:03:56 say,
1:03:56 or are,
1:03:57 or are actually are those flows sort of staying in Europe or going being
1:04:01 directed to other places?
1:04:02 Is that something that you can capture with your analysis?
1:04:06 There's also been quite a bit of talk about,
1:04:08 well,
1:04:09 what,
1:04:09 what are these sort of grains that now we are able to put in the market?
1:04:12 What is that being used for?
1:04:14 Is that being used for food or is that being used for feed?
1:04:17 And does that matter?
1:04:19 Uh,
1:04:19 so again,
1:04:20 how do you take those things into account when you look at these
1:04:23 price changes and you basically map those out into the distribution of,
1:04:26 of income.
1:04:27 And then the last question is looking at your first graph,
1:04:31 when you had the prices,
1:04:32 we already saw quite a bit of food inflation happening before
1:04:35 the war and then of course that gets massively exacerbated.
1:04:38 Um,
1:04:39 so there's obviously more going on than just the word,
1:04:41 and,
1:04:42 and,
1:04:42 and I wonder if you had any reflections on
1:04:44 that and particularly on this whole dichotomy between,
1:04:46 is it about availability or is it about distribution,
1:04:49 right,
1:04:49 which we've talked about a lot on this,
1:04:51 uh,
1:04:52 on this whole issue of,
1:04:53 of food trade.
1:04:54 Um,
1:04:55 and what does that tell us in terms of,
1:04:57 you know,
1:04:57 how could countries manage these kinds of shocks a little bit better?
1:05:00 I mean,
1:05:00 we know these things happen periodically.
1:05:03 Uh,
1:05:03 so what are some of the risk management mechanisms that,
1:05:05 uh,
1:05:06 that could come to mind?
1:05:07 Thanks.
1:05:07 Let me stop there.
1:05:08 That's super interesting again.
1:05:11 Thanks,
1:05:11 Carol.
1:05:12 Lots of questions.
1:05:13 Um,
1:05:13 I,
1:05:13 I propose we just before opening it up to general questions,
1:05:16 we go back to each member,
1:05:18 each presenter,
1:05:19 uh,
1:05:20 do one round.
1:05:21 Is that OK?
1:05:22 Maybe in the order you presented,
1:05:23 um,
1:05:25 so Erhan,
1:05:26 thank you very much,
1:05:27 uh,
1:05:28 lots of stuff for us to think about,
1:05:30 uh,
1:05:30 great comments,
1:05:32 uh,
1:05:32 so.
1:05:35 I
1:05:35 would say some of the comments are about,
1:05:40 for us to think about more,
1:05:41 and some of them are about
1:05:43 how we should think about the analysis.
1:05:45 So I would like to distinguish between these two aspects.
1:05:49 Uh,
1:05:49 first of all,
1:05:50 the,
1:05:51 especially the part about heterogeneity is about the core of the analysis,
1:05:55 and as Carolina mentioned,
1:05:56 it is,
1:05:57 uh,
1:05:58 probably
1:05:59 more women and women and children leaving the conflict areas.
1:06:03 We saw that in the recent invasion,
1:06:05 it was
1:06:06 basically all women and children,
1:06:08 and
1:06:08 uh.
1:06:10 Unfortunately,
1:06:11 we don't have the exact,
1:06:13 uh,
1:06:14 demographics of the,
1:06:16 of the.
1:06:17 Uh,
1:06:19 displaced people,
1:06:20 we have a sub sample of them,
1:06:21 and we can see that,
1:06:23 uh,
1:06:23 the ratio of women and children are a bit higher than others,
1:06:27 but they are not
1:06:28 as extreme as the recent invasion
1:06:31 and
1:06:32 the main problem for our analysis is we don't have this data for the before conflict,
1:06:37 uh,
1:06:38 flows,
1:06:39 so that's why it's not possible for us to to make the analysis more granular,
1:06:43 but.
1:06:44 What the numbers we find are more or less a weighted average of different groups,
1:06:49 and it is weighted
1:06:51 naturally
1:06:52 by the flows
1:06:54 before the conflict,
1:06:55 so
1:06:55 I would imagine that
1:06:57 the
1:06:57 negative impact for women and children
1:07:00 are a bit
1:07:03 counter.
1:07:05 Less since they were they were new movers
1:07:08 so I think for that reason it's important to
1:07:10 think of this as a as a general snapshot and
1:07:13 an average and it is for sure
1:07:16 uh
1:07:17 a lower bound that that we want to emphasize it.
1:07:21 Also,
1:07:22 there are a couple of issues that
1:07:25 At first thought
1:07:27 they might seem to impact the analysis,
1:07:30 but they don't.
1:07:30 For example,
1:07:31 whether the flows are temporary or
1:07:34 permanent,
1:07:34 whether they had assets in the region,
1:07:38 and what
1:07:38 the amenities and other
1:07:41 things to help to
1:07:44 impact their decision,
1:07:44 all these things
1:07:46 we don't need
1:07:47 to know.
1:07:47 Anything about these assumptions
1:07:49 to calculate the welfare impacts.
1:07:52 However,
1:07:52 these are very important topics that we need to think about for a general picture,
1:07:56 and this is one of the things that we studied in the police report to
1:07:59 figure out.
1:08:01 I think the Ukraine government was trying to help the
1:08:03 people in eastern Ukraine and motivate them to move back and
1:08:07 just keep the region
1:08:09 productive.
1:08:10 Uh,
1:08:10 for those decisions,
1:08:12 of course,
1:08:12 the whether these decisions are
1:08:14 permanent or temporary and how we can motivate them to return and what,
1:08:18 how can we help them
1:08:20 by
1:08:20 helping their education and different amenities,
1:08:23 maybe electricity,
1:08:24 housing,
1:08:25 water,
1:08:25 and things like that.
1:08:28 So
1:08:29 that's,
1:08:29 that's,
1:08:30 that's an important
1:08:31 big picture,
1:08:32 uh,
1:08:33 question,
1:08:34 uh,
1:08:35 about the assets in the other side and
1:08:38 we know that
1:08:39 before the recent invasion,
1:08:41 the conflict was
1:08:43 low intensity and people were moving.
1:08:46 Back and forth from the conflict line,
1:08:49 so the contact line was porous,
1:08:51 and
1:08:52 we know that
1:08:53 many people actually stayed in Donbas region
1:08:56 who were displaced from the eastern parts of Donbas and they would
1:08:59 and even those who are in the
1:09:02 uh
1:09:02 separatist controlled areas would go back,
1:09:05 make daily trips to the other side to withdraw
1:09:08 their pension and salaries and things like that.
1:09:11 So
1:09:12 for that reason,
1:09:13 I think many people stayed in that region
1:09:16 and uh
1:09:18 and we don't use those numbers so we use the uh the
1:09:21 the flows to other regions for
1:09:23 for that purpose.
1:09:25 Uh,
1:09:26 and I think that's,
1:09:27 that's all.
1:09:28 Thank you.
1:09:30 Because
1:09:31 Yes,
1:09:32 so thanks a lot on the storage question.
1:09:36 I think definitely what we see for the summer crops is relatively short term.
1:09:42 On the other hand,
1:09:43 I think that storage is probably overrated.
1:09:47 I think,
1:09:47 I mean,
1:09:48 they're exporting,
1:09:49 I mean,
1:09:50 we heard yesterday from the minister they're exporting 9 million tons per month
1:09:55 through the,
1:09:56 even despite the grain blockage,
1:09:58 so they actually expect the silos to be full,
1:10:01 actually,
1:10:02 and,
1:10:02 and of course the donors have been rushing in there.
1:10:06 I think Canada,
1:10:07 Japan,
1:10:08 whatever,
1:10:08 they provided 6 million tons of mobile storage,
1:10:13 which are these silo bags that you can put on the field.
1:10:17 Actually,
1:10:17 I mean,
1:10:17 they're running that thing through the agrarian registry.
1:10:20 If they give us data
1:10:22 on who actually got the storage,
1:10:23 we can evaluate whether that has any impact or not.
1:10:26 And or whether these people,
1:10:28 and of course we do see in the longer term,
1:10:30 and that's why I was referring to the winter crops,
1:10:33 we see a significant drops,
1:10:35 a drop in terms of winter
1:10:37 crop sowing
1:10:38 that is certainly something farmers are sitting on the fence
1:10:42 to see what is going to happen during the in the until the spring,
1:10:47 because even if you don't plant now,
1:10:48 and of course it's a rational decision not to plant now
1:10:51 because you're tying up a lot of capital.
1:10:55 So I think they can substitute for that with summer crops or in the spring.
1:10:59 So I think that will definitely be what will happening until then
1:11:03 will be a significant determinant.
1:11:05 But I think what is important is
1:11:08 that,
1:11:09 I mean,
1:11:09 and there the war,
1:11:10 I mean that we had already hoped that the land reform
1:11:14 will provide a basis for diversifying the agricultural sector in
1:11:18 Ukraine a little bit because I think even globally.
1:11:21 I mean,
1:11:21 you don't see anywhere
1:11:23 farms as large as in Ukraine.
1:11:26 That was one of the reasons why I got into Ukraine in the first place,
1:11:29 because I was surprised
1:11:30 and I didn't,
1:11:32 I didn't get any enlightenment yet,
1:11:34 so I think,
1:11:36 and I mean
1:11:37 people there,
1:11:39 especially before you had
1:11:41 before the land market worked,
1:11:42 I mean
1:11:43 there was no,
1:11:44 no investment,
1:11:45 it's only 5 annual crops,
1:11:48 it's soybean,
1:11:49 it's
1:11:50 Wheat,
1:11:51 sunflower,
1:11:52 and maize,
1:11:53 and that is what they grow year in,
1:11:55 year out,
1:11:56 which is very damaging to the,
1:11:58 I mean,
1:11:59 I think they're mining the soil,
1:12:01 or at least there is some soil mining going on that is in terms of
1:12:05 long term fertility and of course it's very labor extensive,
1:12:08 so I think what we are seeing,
1:12:09 and that's where I think that 50 million grant that the EU provided
1:12:13 was actually quite interesting because all of that went to people.
1:12:17 I mean,
1:12:17 We did some very,
1:12:19 I mean,
1:12:20 our team did some checks.
1:12:22 They are doing
1:12:23 strawberries,
1:12:24 they're doing raspberries,
1:12:25 they're doing very high value crops and with drip
1:12:28 irrigation and that of course that generates very high returns
1:12:31 and that is something that I think you can actually,
1:12:34 especially in the areas that are not conflict affected right now
1:12:38 and even if and if there is credit,
1:12:40 I think we had.
1:12:41 I mean,
1:12:42 so I think if,
1:12:42 if the financial sector works,
1:12:44 you can actually draw in a lot of private capital
1:12:47 to support that diversification,
1:12:49 and that is something that I think
1:12:52 there are huge opportunities despite the war going on because
1:12:56 a large part of the of the country is not really
1:12:59 that much affected by the conflict.
1:13:04 Thanks,
1:13:05 over to you,
1:13:05 Bob.
1:13:06 Yeah,
1:13:07 thanks,
1:13:07 Carolina for the excellent questions.
1:13:09 So it's indeed true that,
1:13:11 uh,
1:13:11 prices have come down.
1:13:13 Uh,
1:13:14 quite a bit after,
1:13:15 uh,
1:13:16 sort of these restrictions on trade and experts were,
1:13:19 uh,
1:13:20 alleviated,
1:13:21 but we believe that sort of the initial jump in prices
1:13:24 reflects both.
1:13:27 Uh,
1:13:27 supply disruptions,
1:13:28 but also
1:13:29 stockpiling
1:13:30 and uncertainty,
1:13:32 and those two latter forces,
1:13:33 stockpiling and uncertainty,
1:13:34 are not forces we capture very well
1:13:36 with our model.
1:13:37 So when we sort of do a sort of a
1:13:39 calibration exercise and we try to verify
1:13:42 whether the price changes predicted by our model
1:13:44 match those that we observe in reality,
1:13:46 we see that they're slightly lower,
1:13:48 like ballpark,
1:13:49 you know,
1:13:49 they're in the same ballpark but they're lower,
1:13:51 and we think that's because,
1:13:52 you know,
1:13:52 our models abstracts from these,
1:13:54 these considerations.
1:13:55 Um,
1:13:56 so to answer your question sort of.
1:13:59 Is food really reaching these developing countries?
1:14:02 I don't have a good answer to that.
1:14:04 Yes.
1:14:04 Like,
1:14:04 usually we would,
1:14:05 for instance,
1:14:05 like look at the Comrade,
1:14:06 but
1:14:08 Those data are not sort of uh up to date yet so
1:14:11 we'll only be able to tell in a in a few months from now
1:14:14 and your second question,
1:14:15 you know,
1:14:15 as to sort of,
1:14:16 you know,
1:14:17 the sort of broader longer term
1:14:20 impacts on on uh
1:14:21 food markets,
1:14:22 for instance,
1:14:23 sort of
1:14:24 the,
1:14:24 the
1:14:25 evolution of the pandemic and,
1:14:26 and,
1:14:27 and climate change.
1:14:29 Uh,
1:14:30 my sense is very much that those have like contributed to,
1:14:32 to sort of persistently
1:14:33 higher,
1:14:34 uh,
1:14:34 food prices.
1:14:35 So for instance,
1:14:35 in the FT
1:14:36 today sort of there's an article talking
1:14:39 precisely,
1:14:39 uh,
1:14:40 about this
1:14:41 in principle,
1:14:42 sort of,
1:14:42 you know,
1:14:42 our model,
1:14:44 uh,
1:14:45 would accommodate,
1:14:46 uh,
1:14:46 this type of,
1:14:47 uh,
1:14:48 shock.
1:14:48 So I think sort of framework we have set up sort of,
1:14:51 uh,
1:14:51 lends itself to,
1:14:52 to sort of analysis of these,
1:14:53 but we haven't explicitly.
1:14:55 Uh,
1:14:55 incorporated these because we wanted to isolate,
1:14:57 uh,
1:14:58 the impact of,
1:14:59 of the war but there's no question that they're of course very,
1:15:01 very important.
1:15:07 Thanks everyone.
1:15:08 Carol,
1:15:09 I will,
1:15:09 I will come back to you,
1:15:10 to give you a chance if you have any reactions to any of these,
1:15:12 but we have one question online,
1:15:15 uh,
1:15:15 but I wanted to see if there's any questions in the room.
1:15:19 Um
1:15:22 Maybe we'll go to,
1:15:23 to,
1:15:23 to Korum,
1:15:24 who's online on the Webex.
1:15:26 Khuram,
1:15:27 are you still there?
1:15:27 Do you want to come in and ask you,
1:15:29 you had two questions.
1:15:30 If you could pose them briefly.
1:15:38 Uh,
1:15:38 Korum,
1:15:40 let me see,
1:15:40 are you still there?
1:15:42 Am I audible?
1:15:44 Yes,
1:15:44 we can hear you.
1:15:45 Hello.
1:15:46 Yes.
1:15:46 My first question was that it has been stated in one of the presentations,
1:15:50 the last one,
1:15:51 I suppose,
1:15:53 that the
1:15:55 shortages in the global wheat supply would benefit Iraq and Pakistan.
1:16:00 Uh,
1:16:00 but now,
1:16:01 uh,
1:16:01 the government in Pakistan has been issuing warning,
1:16:04 uh,
1:16:05 signals of possible import of wheat around 5 million tons.
1:16:10 Uh,
1:16:11 next year because of a reduction in the sowing area of wheat crop
1:16:15 due to the devastating floods.
1:16:17 So how these two opposing hypotheses can be reconciled?
1:16:21 So,
1:16:22 this is my first question
1:16:23 that can be answered.
1:16:27 So why don't you go ahead with your second one as well and we'll do a roundup.
1:16:31 OK.
1:16:32 OK.
1:16:33 My second question was that
1:16:35 the diversion of the funds from the G7 countries and EU
1:16:40 to the humanitarian and war effort in
1:16:43 Ukraine has deprived the funds to the third world countries who are uh
1:16:48 uh.
1:16:49 Facing uh
1:16:52 uh
1:16:53 quite a bit economic
1:16:55 meltdown because of,
1:16:57 uh,
1:16:58 uh,
1:17:00 global food prices and also oil prices.
1:17:05 Uh,
1:17:05 so how can,
1:17:07 uh,
1:17:07 it can be
1:17:10 said that these countries could
1:17:13 It should also be
1:17:14 taken as a factor in the analysis
1:17:17 when the impact of the war is being
1:17:22 studied,
1:17:23 so this is my second question.
1:17:26 Great,
1:17:26 thank you.
1:17:27 I,
1:17:27 I guess,
1:17:27 and then we had another question on YouTube,
1:17:29 but I,
1:17:30 I'll,
1:17:30 I'll fold it into that second point,
1:17:31 but if I could expand maybe.
1:17:34 Both of these,
1:17:35 um,
1:17:36 on the first one,
1:17:37 I mean,
1:17:37 obviously there's a lot more going on,
1:17:39 the floods in Pakistan case in point,
1:17:41 but,
1:17:41 but how do you see,
1:17:43 you know,
1:17:44 what you're isolating from your model in terms of all that?
1:17:46 I mean,
1:17:46 Carol already alluded to,
1:17:47 oh,
1:17:48 things are changing,
1:17:48 how up to date is this?
1:17:49 and
1:17:50 uh what are your plans for expanding on this or or is is is is that sort of
1:17:56 for future work for people to do through,
1:17:58 through the online systems?
1:17:59 And then the second question,
1:18:00 I guess
1:18:01 we could expand that a little bit to think more generally about,
1:18:03 you know,
1:18:04 there's a lot of players here.
1:18:05 I mean,
1:18:05 Klaus kind of alluded to that a little bit with the EU and
1:18:09 other players.
1:18:09 I mean,
1:18:10 there was a question uh in,
1:18:11 in,
1:18:12 on YouTube about FAO,
1:18:14 um.
1:18:15 But I guess more generally and maybe I don't
1:18:18 even know if there's really a question here and
1:18:21 just how do we think about sort of
1:18:23 maybe this is to Carol actually
1:18:25 working in a space where there are so many players and
1:18:28 maybe the question is where do you see the bank's role,
1:18:31 I guess maybe let's put it that way.
1:18:32 Let's make it sort of
1:18:33 in,
1:18:33 in the space of all these different actors responding in Ukraine.
1:18:38 What is,
1:18:38 I mean you alluded to that a little bit at the very beginning,
1:18:41 but what,
1:18:41 what do you see our role going forward over the sort of short to medium term?
1:18:45 Uh,
1:18:46 in this.
1:18:46 So maybe,
1:18:47 maybe,
1:18:47 I don't know if Bob,
1:18:48 you wanna sort of take that first one and then maybe Carol
1:18:50 for the second unless Klaus you want to come in on,
1:18:52 on the second.
1:18:55 Yeah,
1:18:55 so thanks a lot for the,
1:18:57 the,
1:18:57 the comment,
1:18:58 uh,
1:18:58 Khuram.
1:18:58 So
1:18:59 I should perhaps have stressed more that sort of
1:19:02 in our model sort of we keep
1:19:04 all these other factors
1:19:06 like
1:19:06 constant so we do not accommodate,
1:19:08 uh,
1:19:09 for instance,
1:19:10 you know,
1:19:10 unusual,
1:19:11 uh,
1:19:11 weather shocks.
1:19:13 And of course they're very important,
1:19:15 but that's precisely why
1:19:16 we have sort of these country specific online,
1:19:19 uh,
1:19:20 tools
1:19:21 that you can use sort of for your own,
1:19:23 uh,
1:19:23 analysis where
1:19:25 those of us sort of a better knowledge of the,
1:19:27 the specific country you're working on
1:19:29 can actually use that information and simulate
1:19:31 what the effect would be if you do take
1:19:33 those type of events,
1:19:35 uh,
1:19:36 into consideration.
1:19:38 So,
1:19:42 Great.
1:19:43 Carol,
1:19:43 I mean,
1:19:43 I realize it's a bit of an open-ended question to you and,
1:19:46 and maybe a sensitive one,
1:19:47 so please feel free to,
1:19:48 to,
1:19:48 to answer it in the way you'd like.
1:19:51 It's now happy to say a few words.
1:19:53 I mean,
1:19:53 it's a really,
1:19:54 it's a very fluid landscape,
1:19:56 right?
1:19:56 Because as you said,
1:19:58 obviously Ukraine,
1:20:00 I think has mobilized an enormous amount of support,
1:20:03 it being uh considered part of Europe,
1:20:05 of course,
1:20:06 you know,
1:20:06 the European Union and,
1:20:08 and European actors are very focused on,
1:20:10 on it,
1:20:10 so is the US so,
1:20:11 so there's lots of moving pieces,
1:20:13 right,
1:20:13 when it comes to helping Ukraine.
1:20:15 So what's up,
1:20:16 what,
1:20:16 what role have we been playing within that landscape?
1:20:18 Um.
1:20:19 So,
1:20:19 a couple of things maybe to say.
1:20:21 First,
1:20:22 we are,
1:20:22 I think,
1:20:23 the only game in town
1:20:25 excluding local researchers,
1:20:27 of which,
1:20:28 you know,
1:20:28 there are some who are doing work that is actually building an evidence base,
1:20:32 and I include in,
1:20:34 in that,
1:20:34 the work that we just saw today and another work that I know.
1:20:38 You guys are doing.
1:20:39 I also,
1:20:40 you know,
1:20:41 mean by that the,
1:20:42 the rapid,
1:20:42 um,
1:20:43 damages and needs assessment.
1:20:45 So we are on what we are one of the few
1:20:47 actors that is actually trying to bring some data and some evidence
1:20:51 to the discussion on,
1:20:52 on what support is needed,
1:20:54 you know,
1:20:54 in what sectors,
1:20:55 what locations,
1:20:56 what the magnitude of the impacts is,
1:20:59 and so on.
1:20:59 And of course,
1:21:00 you know,
1:21:00 this is a high capacity government,
1:21:01 so they do have data themselves,
1:21:03 you know,
1:21:03 so in many cases we are doing that.
1:21:06 Uh,
1:21:06 together with the government,
1:21:07 I mean,
1:21:08 Claus was also alluding to his relationships with,
1:21:10 uh,
1:21:10 with the Ministry of Agriculture and others,
1:21:12 but I think that's an important role that we are playing,
1:21:15 right?
1:21:15 Trying to,
1:21:16 again,
1:21:17 to the extent possible,
1:21:18 have some of these discussions being anchored
1:21:20 in,
1:21:20 in data and evidence.
1:21:23 And then the other role that,
1:21:24 that we are playing,
1:21:26 and this is obviously more
1:21:29 And about the technical level because of course the political level,
1:21:32 you know,
1:21:32 follows uh a different sort of dynamic is.
1:21:36 To the extent possible trying to ensure that that we are all somewhat coordinated,
1:21:41 particularly around some of these very critical sectors where,
1:21:44 where everybody's focusing,
1:21:45 right?
1:21:46 So energy
1:21:47 is right now receiving a lot of attention because
1:21:50 if you're following the news you all probably know that
1:21:53 um
1:21:54 the latest sort of Russian attacks are very focused on damaging the
1:21:58 energy infrastructure uh because that's gonna have a pretty dire effect.
1:22:02 In the middle of the winter,
1:22:03 if heating is not available,
1:22:05 electricity is not available,
1:22:06 and so on.
1:22:07 So,
1:22:07 so,
1:22:07 there are lots of sort of actors,
1:22:08 you know,
1:22:09 coming into that space,
1:22:10 trying to help.
1:22:11 And I think at the technical level,
1:22:13 working with the fund,
1:22:14 working with some of the other development banks that are active in Ukraine,
1:22:19 we've tried to sort of put forward ideas that
1:22:22 You know,
1:22:22 that sort of help us all coordinate,
1:22:25 you know,
1:22:25 and play to our comparative advantage,
1:22:26 but everybody's kind of aware of what,
1:22:28 of what everybody else is doing and,
1:22:29 you know,
1:22:30 it's,
1:22:30 it's super time consuming but it's helpful because
1:22:33 ultimately,
1:22:34 we are not duplicating,
1:22:35 we are complementary,
1:22:36 and,
1:22:37 and again,
1:22:37 you know,
1:22:37 we're all ultimately working with the same actors and,
1:22:40 and they are overwhelmed.
1:22:41 I mean,
1:22:41 we tend to forget.
1:22:43 Because they're capable,
1:22:44 very capable,
1:22:44 but you know,
1:22:45 we don't get them to forget that they're sort of fighting a war,
1:22:47 right on the other side.
1:22:48 So,
1:22:49 you know,
1:22:49 so you have to kind of
1:22:50 modulate your expectations.
1:22:52 And then,
1:22:52 of course,
1:22:53 you know,
1:22:53 whatever we can,
1:22:54 we are providing
1:22:55 financial resources,
1:22:57 although we are a little bit constrained in that space
1:23:01 because,
1:23:01 you know,
1:23:01 Ukraine is not an either country,
1:23:03 so they don't have access to either resources.
1:23:06 And the bank,
1:23:07 it's,
1:23:08 uh,
1:23:08 it was already quite exposed in Ukraine before the conflict
1:23:12 and therefore the margin that we have for new lending
1:23:15 is quite limited.
1:23:16 So we've been very dependent,
1:23:18 as I said in my introduction,
1:23:21 uh,
1:23:21 on,
1:23:22 you know,
1:23:22 countries being willing to put money for Ukraine
1:23:25 and to channel that money through us.
1:23:27 So a lot of what we've done on the financial side.
1:23:29 Again,
1:23:30 hasn't been with our resources,
1:23:31 but it's been a facilitator role
1:23:34 helping others that want to give to Ukraine,
1:23:37 you know,
1:23:37 manage that transaction and then putting some sort of
1:23:40 controls and,
1:23:41 and monitoring mechanisms around that.
1:23:43 So that,
1:23:44 that I think it's kind of the role that we are playing,
1:23:45 but again it's a very fluid,
1:23:47 uh,
1:23:48 very,
1:23:48 very fluid landscape and I suspect that things will
1:23:51 continue to evolve over the next few months.
1:23:55 Great.
1:23:56 Carol,
1:23:56 thanks.
1:23:56 I,
1:23:56 I,
1:23:57 I actually think that's probably a good place to end the,
1:23:59 the,
1:23:59 the,
1:23:59 the discussion.
1:24:01 Um,
1:24:02 I mean,
1:24:02 fascinating to hear sort of the complexities
1:24:04 on the ground and your reflections on how
1:24:07 this kind of research both in terms of the tools that it's
1:24:10 bringing and the results that it's bringing
1:24:13 can sort of contribute to,
1:24:14 to,
1:24:15 to moving our agenda forward within the country and then as we saw also globally,
1:24:19 um,
1:24:20 So obviously this is the,
1:24:21 this is,
1:24:21 this is uh uh an ongoing and unfolding process of,
1:24:26 of,
1:24:26 of both what's happening on the ground and the process of,
1:24:28 of
1:24:29 providing tools and,
1:24:30 and,
1:24:30 and research and we hope to
1:24:32 definitely to be able to,
1:24:33 to continue doing that.
1:24:34 So
1:24:35 with that,
1:24:35 thank you everybody for coming and for your attention,
1:24:37 for those in the room and those online
1:24:39 and uh please join me in thanking the presenters and our discussion today.
1:24:43 Thank you.
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